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Hades style textual inversion
Textual Inversion trained on art from the Hades game, tested on Anything V3 model. Put this TI in the embeddings folder, type "Hades-8500" in prompt to activate. Also I recommend to use words "realistic", "cartoon",...
SD3.5 Large Canny-guided images
Generate new images from a prompt while following edges extracted from a reference image. Outputs include the generated image batch and one reference edge-map preview. For a first run, upload a reference image and replace the example prompt with your desired subject, colors, setting, and style. Keep output and reference dimensions at 1024 × 1024, Canny thresholds at 0.3 and 0.6, control strength at 0.66, and denoise at 1. The fixed SD3.5 Large checkpoint and matching Canny ControlNet are used together. Control strength sets how strongly reference edges influence generation. Set it to 0 for generation without edge influence; the reference upload is still required and its edge map is still returned. Guidance start and end are fractions of the denoising schedule from 0 to 1. Keep start at or below end. Reference resizing and generated output dimensions are independent. Match their aspect ratios for consistent framing. Center crop can remove content near the reference borders; disabled cropping resizes to the requested shape and can stretch the image. Reference interpolation changes resizing before edge detection, not generated output dimensions. Inspect the edge preview when changing crop, interpolation, or thresholds. Keep the low threshold below the high threshold; lower thresholds retain more faint detail, while higher thresholds select stronger contours. Variations sets the number of generated images using the same prompt and edge map. Sampling offers seed, steps, prompt guidance scale (CFG), sampler, scheduler, and denoise. Seed -1 selects a fresh random seed each run; use a fixed seed to compare settings with the same initial noise. Begin with the supplied sampling defaults. Denoise 1 uses the full generation schedule; lower values shorten denoising from the empty latent. The reference supplies edges rather than an initial image latent, so this tool does not preserve uploaded pixels. Uploaded transparency is not carried through the connected image path. Negative conditioning is zeroed internally; there is no editable negative prompt.
Anima image generation
Generate square images from text with Anima, using standard sampling or Turbo mode. No source image is required; the result is an image batch returned through Sweet Tea's preview transport, without permanent ComfyUI image saving. For a first run, replace the example positive prompt with your subject, composition, lighting and style. Review the negative prompt for attributes you want to exclude. Leave image count at 1 and Turbo mode disabled, then run with the supplied sampling defaults. Seed -1 produces fresh randomization on each run; use a fixed seed to compare changes with the same initial noise. Output size is fixed to the source's square, approximately 1-megapixel setting, about 1024 × 1024 pixels with dimensions rounded to multiples of 16. This interface has no resolution control. Image count determines the number of returned images; larger batches require more memory. Turbo mode selects the supplied Anima Turbo LoRA together with separate Turbo steps and guidance. Standard defaults are 30 steps and CFG 4; Turbo defaults are 8 steps and CFG 1. Only the active mode's steps and guidance affect sampling. Turbo LoRA strength affects the selected model only in Turbo mode. At CFG 1, negative conditioning does not influence ordinary classifier-free guidance. Advanced controls include both modes' steps and guidance, Turbo LoRA strength, seed, sampler, scheduler and denoise strength. Keep Euler, the simple scheduler and denoise strength 1 for the first run. Steps must be within 1–10000 and CFG within 0–100, even though their primitive input fields accept broader values. Lower denoise strength shortens the noise schedule; this text-to-image workflow has no source image to preserve. The runtime needs these source-specified model files in the corresponding ComfyUI model directories: [anima-base-v1.0.safetensors](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/diffusion_models/anima-base-v1.0.safetensors) in models/diffusion_models; [anima-turbo-lora-v0.2.safetensors](https://huggingface.co/circlestone-labs/Anima-Official-LoRAs/resolve/main/anima-turbo-lora-v0.2.safetensors) in models/loras; [qwen_3_06b_base.safetensors](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/text_encoders/qwen_3_06b_base.safetensors) in models/text_encoders; and [qwen_image_vae.safetensors](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/vae/qwen_image_vae.safetensors) in models/vae. The selected output also requires [Sweet Tea nodes](https://github.com/tea-time-labs/sweet-tea-nodes). Standard and Turbo share the same image output.
SDXL two-reference image generation
Create new images with SDXL Base using a positive prompt and visual conditioning from two uploaded reference images. Both references guide the same generated result. For a first run, upload both references, describe the desired subject and style, and keep the 1024 × 1024 output size, reference strengths of 0.75, and denoise amount of 1. Use the negative prompt to describe unwanted features. Image count sets the number of images generated per run with the same prompts and references. Adjust each reference strength independently to change its contribution. Zero removes that reference's conditioning influence; setting both strengths to zero leaves text conditioning. Both uploads remain required in every case. Reference noise augmentation perturbs the corresponding image embedding rather than adding visible grain to the result. The references guide generation through image embeddings. Sampling starts from an empty latent; this is not pixel editing and does not preserve exact reference layouts or uploaded transparency. Center cropping can omit content near reference-image edges. Under advanced reference framing, choose none to avoid center cropping. Under advanced Sampling, use seed -1 for fresh randomization or a fixed seed to compare settings with the same initial sampling noise. Steps controls the number of denoising steps, CFG controls conditioning guidance, and sampler and scheduler select the sampling algorithm and noise schedule. Denoise controls the sampling denoise amount; lowering it does not preserve reference pixels because neither reference supplies the starting latent. Larger dimensions and image counts require more memory. The workflow uses the fixed sd_xl_base_1.0.safetensors checkpoint and clip_vision_g.safetensors vision encoder.
FLUX.2 Klein two-image editing
Generate edited images from a source image, a second visual reference and written instructions using FLUX.2 Klein 9B. For a first run, upload both required images: the main image as Figure 1 and the visual reference as Figure 2. Replace the example instructions with what to change, what to take from Figure 2 and what to retain from Figure 1. For example, request clothing from Figure 2 on the subject in Figure 1 and describe the desired setting. Keep the supplied defaults of 1 output megapixel, 1 reference megapixel, 1 variant, 4 steps, CFG 1 and Euler sampling, then run to receive an edited image. Output megapixels sets Figure 1's resized area in millions of pixels. Those dimensions determine generation size and scheduling, so changing this control also changes Figure 1's reference resolution. Output proportions follow Figure 1, subject to dimension rounding. Reference megapixels independently sets Figure 2's working resolution without setting output size. Each image has its own resize interpolation control. Advanced controls let you request a batch with Variants, choose sampling steps and algorithm, and adjust CFG between the instructed and zero-text conditioning branches. Both branches carry both image references. Seed -1 selects fresh sampling noise each run; use a fixed seed to compare changes with the same initial noise. Both images condition a newly generated result. Exact preservation of pose, identity or unchanged pixels is not guaranteed. This tool has no mask, denoise control or editable negative prompt, and uploaded transparency is not retained through the RGB generation path. Results are returned to Sweet Tea for preview rather than saved by the original ComfyUI SaveImage node.
Seedance image to video
Animate a starting image into one video with prompted action and camera movement, with optional generated audio on Seedance 1.5 Pro. Upload the required image to use as the first frame, then replace the example prompt with action, camera movement and atmosphere suited to your image. For a first run, keep Seedance 1.5 Pro, 480p resolution, adaptive aspect ratio and the five-second duration. Run the Pipe to receive the generated video in Sweet Tea. Duration is measured in seconds and supports 3–12 seconds. The example requests four shots, dialogue and ambience; simplify it or increase the duration to allow more room for the action. Prompted cuts, dialogue and camera effects are creative directions rather than guaranteed timing. Choose an explicit aspect ratio when you need a particular frame shape. Resolution offers 480p, 720p and 1080p, subject to the selected model's support. Model selects among the supplied Seedance 1.5 Pro, 1.0 Pro, 1.0 Lite and 1.0 Pro Fast choices. Generate audio applies only to Seedance 1.5 Pro; the other choices ignore it. With audio enabled, describe desired dialogue and background sounds in the prompt. Fixed camera adds a stationary-camera instruction but does not guarantee a motionless viewpoint; remove conflicting pans, dollies and other camera movement from your prompt when using it. Seed -1 selects a new random value each run; a fixed seed controls generation randomization. AI watermark requests an AI-generated watermark on the video. The uploaded image's alpha mask is unused, so this Pipe does not preserve uploaded transparency. Generation uses the ByteDance provider and involves transferring the input image and prompt to that provider and a provider-priced generation call.
Bad_NegAnatomy Textual Inversion
his is a Textual Inversion I have created, this is mainly useful to improve your overall aesthetics and character anatomy.
Cherry Nobody
Follow me to make sure you see new poses and Nobodys when I post them. Things move fast on this site, it's easy to miss.
HiDream I1 Dev text to image
Create images from a written description using the fixed HiDream I1 Dev FP8 model and its four text encoders. No uploaded image is required. For a first run, replace the positive prompt and keep the supplied 1024 × 1024 size, one image, 28 steps, LCM sampler, normal scheduler, CFG 1 and sampling shift 6. Seed -1 selects fresh randomness on each run. Increase Images per run to generate a batch; larger dimensions and batches require more memory. Width and height are measured in pixels and use multiples of 16. The negative prompt is connected to negative conditioning, but has no effect at the default CFG of 1. It becomes relevant when CFG is increased above 1. The supplied settings are the source's Dev configuration; changing CFG, sampler, scheduler or sampling shift is experimental tuning rather than a model variant selector. This tool generates from an empty latent. It does not accept a source image or provide image editing.
Qwen 2511 image edit
Edit a source image with a written instruction using Qwen Image Edit 2511. For a first run, upload an image, describe the changes you want and the features to retain, and keep the supplied defaults: denoise strength 1, 40 sampling steps, CFG guidance 4, Euler sampler and Simple scheduler. The example instruction requests anime styling while retaining identity. Leave the negative prompt empty unless you want to describe attributes to discourage. Seed -1 randomizes the sampling noise. The image is automatically resized before editing. The resized RGB image supplies both positive and negative reference conditioning and is encoded as the initial sampling latent. Lower denoise values retain more of that initial image's structure. CFG guidance adjusts prompt conditioning separately from denoise strength. A fixed nonnegative seed reuses the noise setting; changing the image, instruction or sampling controls can still change the result. Output dimensions come from the automatically scaled source image. This tool has no manual output-size control, text-only mode, mask-based editing input or separate output-upscaling stage. Uploaded alpha transparency is not preserved because the loader's alpha-mask output is not connected. The selected output returns the decoded edited image. Advanced settings include negative prompting, guidance normalization strength, normalization before CFG and sampling shift. Normalization defaults to strength 1 with pre-CFG normalization off, using post-CFG attenuation. Enabling pre-CFG normalization rescales noise before CFG combination without clamping and can amplify it. Sampling shift defaults to 3.1 and changes the model's sampling configuration independently of denoise and CFG. The workflow uses qwen_image_edit_2511_bf16.safetensors, qwen_2.5_vl_7b_fp8_scaled.safetensors as its Qwen image text encoder, and qwen_image_vae.safetensors.
